Improving Diversity with Multi-Loss Adversarial Training in Personalized News Recommendation

作     者：Ruijin Xue Shuang Feng Qi Wang 

作者机构：School of Computer and Cyber SciencesCommunication University of ChinaBeijing100024China State Key Laboratory of Media Convergence and CommunicationCommunication University of ChinaBeijing100024China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第80卷第8期

页      面：3107-3122页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research was funded by Beijing Municipal Social Science Foundation(23YTB031) the Fundamental Research Funds for the Central Universities(CUC23ZDTJ005) 

主　　题：News recommendation diversity accuracy data augmentation 

摘      要：Users’interests are often diverse and multi-grained,with their underlying intents even more *** captur-ing users’interests and uncovering the relationships between diverse interests are key to news ***,diversity is an important metric for evaluating news recommendation algorithms,as users tend to reject excessive homogeneous information in their recommendation ***,recommendation models themselves lack diversity awareness,making it challenging to achieve a good balance between the accuracy and diversity of news *** this paper,we propose a news recommendation algorithm that achieves good performance in both accuracy and *** most existing works that solely optimize accuracy or employ more features to meet diversity,the proposed algorithm leverages the diversity-aware capability of the ***,we introduce an augmented user model to fully capture user intent and the behavioral guidance they might undergo as a ***,we focus on the relationship between the original clicked news and the augmented clicked ***,we propose an effective adversarial training method for diversity(AT4D),which is a pluggable component that can enhance both the accuracy and diversity of news recommendation *** experiments on real-world datasets confirm the efficacy of the proposed algorithm in improving both the accuracy and diversity of news recommendations.